One conceptual detail for assessing convergence is that, due to the nonparametric nature of the model, the number of parameters which could potentially be monitored to measure auto-correlation of the chains or effective sample size varies.
The location parameters of the clusters, for instance, vary at each iteration, and even the labels of the clusters vary, which makes it tricky to follow.
However, it is possible to monitor the log-likelihood of the data along the iterations, the value of the latent variable \(u\) , the number of components and for the semi-parametric model, the value of the common scale parameter.
The following code shows how to compute the potential scale reduction factor [1]}:
